Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Mara S. Bernardi"'
Autor:
Simone Vantini, Alessandra Menafoglio, Pasquale Claudio Africa, Mara S. Bernardi, Luca Formaggia, Carlo de Falco
Publikováno v:
Mathematical Geosciences. 53:1781-1812
Recent advances in satellite technologies, statistical and mathematical models, and computational resources have paved the way for operational use of satellite data in monitoring and forecasting natural hazards. We present a review of the use of sate
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ca4bf0fab500646000d28756e94dfcbc
https://doi.org/10.1007/s11004-021-09948-8
https://doi.org/10.1007/s11004-021-09948-8
Publikováno v:
Journal of Multivariate Analysis. 167:15-30
We consider the problem of analyzing spatially distributed data characterized by spatial anisotropy. Following a functional data analysis approach, we propose a method based on regression with partial differential regularization, where the differenti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::71ced09d3832589f056782918b1b27e2
https://doi.org/10.1016/j.jmva.2018.03.014
https://doi.org/10.1016/j.jmva.2018.03.014
Autor:
Matteo Pelucchi, Alberto Cuoci, Alessio Frassoldati, Tiziano Faravelli, Laura M. Sangalli, Alessandro Stagni, Piercesare Secchi, Mara S. Bernardi
Publikováno v:
Combustion and Flame. 168:186-203
The increasing number of experimental data, accurate thermodynamic and reaction rate parameters drive the extension, revision, and update of large size kinetic mechanisms. Despite these detailed mechanisms (i.e. the models) generally allow good predi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5f0fcab013f8c9de617f2acd6525609b
https://doi.org/10.1016/j.combustflame.2016.03.019
https://doi.org/10.1016/j.combustflame.2016.03.019
Publikováno v:
Stochastic Environmental Research and Risk Assessment. 31:23-38
We propose a method for the analysis of functional data with complex dependencies, such as spatially dependent curves or time dependent surfaces, over highly textured domains. The models are based on the idea of regression with partial differential r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4786460d60e83559db50214716854a34
https://doi.org/10.1007/s00477-016-1237-3
https://doi.org/10.1007/s00477-016-1237-3
Publikováno v:
Electron. J. Statist. 8, no. 2 (2014), 1817-1824
We analyze the juggling data by means of the $k$-mean alignment algorithm using cycles as the experimental units of the analysis. Allowing for affine warping, we detect two clusters distinguishing between mainly-planar trajectories and trajectories t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cc94b0ad252d5978e54da78c641e3483
http://projecteuclid.org/euclid.ejs/1414588168
http://projecteuclid.org/euclid.ejs/1414588168
Publikováno v:
Electron. J. Statist. 8, no. 2 (2014), 1714-1723
We analyze the proteomics data introducing a block $k$-mean alignment procedure. This technique is able to jointly align and cluster the data, accounting appropriately for the block structure of these data, that includes measurement repetitions for e
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e99c0bf3a0f6692abc03424919e73c93
http://hdl.handle.net/11311/876557
http://hdl.handle.net/11311/876557